CS 570 Machine Learning 4 cr.  (4-0-0)

In this course, we study algorithms for writing computer programs that improve with experience.  The basic theory of machine learning will be covered, including concepts such as hypothesis space, bias, overfitting, training sets, and testing sets.  We will also dive into the details of specific machine learning techniques, including decision trees, Bayesian classifiers, instance-based learning, and artificial neural networks. The course is focused primarily on supervised learning, though other approaches (semi-supervised, un-supervised, and reinforcement learning) will be explored as well.